| Citation: | GUAN Kaigang, SU Weiyi, CUI Sheng, et al. POD-BPNN prediction on the three-dimensional complex flow field of hypersonic waverider forebody/inlet[J]. Journal of Aerospace Power, 2025, 40（2）：20220536 doi: 10.13224/j.cnki.jasp.20220536
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A fast prediction model for the flow field was constructed based on proper orthogonal decomposition (POD) and back propagation neural network (BPNN) for a three-dimensional integrated waverider forebody-inlet. Moreover, the hypersonic three-dimensional flow fields were predicted under different Mach number, angle of attack and back pressure. The research showed that the prediction model can accurately predict the flow field of non-sample conditions in the sampling space. The errors of fast prediction model for the Mach number, pressure, and temperature were less than 0.5%, 3.8% and 1.1%, respectively. The predictive results of the flow field for the waverider forebody, pressure distribution and the main performance parameters were highly consistent with the CFD (computational fluid dynamics) results. The prediction model had certain prediction ability for the external working conditions of the sampling space. However, the flow field numerical simulation adopted steady calculation, while the shock string structure in the isolator was affected by the separated vortex and had unsteady characteristics, indicating a large prediction error in the shock string area of the isolator.
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